Hanyue Zhang
ABSTRACT
Traditional cross-language similarity assessment techniques mostly rely on the theories of linguistics and pragmatics, which is also bound up with the natural features of "natural language". This paper mainly studies the similarity comparison algorithm of "blank" concept in Chinese and Western poetics under the background of Internet. In this paper, a sentence-level cross-language similarity assessment framework (SCLSE) is proposed. The framework is based on word embedding as the underlying vector representation, which is used to learn the semantic representation of sentences through the fusion of various neural network structures, and finally outputs the similarity score of sentences. In this paper, we also divide the short text into paragraphs and treat the paragraphs as long sentences as sequence input to realize the iterative calculation of similarity on a larger scale. In this paper, we set up different comparative experiments to verify the effectiveness and application value of SCLSE framework in the cross-language text similarity assessment task under different text unit granularity.
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